"""Unsupervised Outlier Detection Using
Empirical Cumulative Distribution Functions (ECOD)
"""
# Author: Zheng Li <jk_zhengli@hotmail.com>
# Author: Yue Zhao <yzhao062@gmail.com>
# License: BSD 2 clause


import warnings

import matplotlib.pyplot as plt
import numpy as np
from joblib import Parallel, delayed
from scipy.stats import skew as skew_sp
from sklearn.utils import check_array

from .base import BaseDetector
from .sklearn_base import _partition_estimators
from ..utils.stat_models import column_ecdf


def skew(X, axis=0):
    return np.nan_to_num(skew_sp(X, axis=axis))


def _parallel_ecdf(n_dims, X):
    """Private method to calculate ecdf in parallel.
    Parameters
    ----------
    n_dims : int
        The number of dimensions of the current input matrix

    X : numpy array
        The subarray for building the ECDF

    Returns
    -------
    U_l_mat : numpy array
        ECDF subarray.

    U_r_mat : numpy array
        ECDF subarray.
    """
    U_l_mat = np.zeros([X.shape[0], n_dims])
    U_r_mat = np.zeros([X.shape[0], n_dims])

    for i in range(n_dims):
        U_l_mat[:, i: i + 1] = column_ecdf(X[:, i: i + 1])
        U_r_mat[:, i: i + 1] = column_ecdf(X[:, i: i + 1] * -1)
    return U_l_mat, U_r_mat


class ECOD(BaseDetector):
    """ECOD class for Unsupervised Outlier Detection Using Empirical
    Cumulative Distribution Functions (ECOD)
    ECOD is a parameter-free, highly interpretable outlier detection algorithm
    based on empirical CDF functions.
    See :cite:`li2021ecod` for details.

    Parameters
    ----------
    contamination : float in (0., 0.5), optional (default=0.1)
        The amount of contamination of the data set, i.e.
        the proportion of outliers in the data set. Used when fitting to
        define the threshold on the decision function.

    n_jobs : optional (default=1)
        The number of jobs to run in parallel for both `fit` and
        `predict`. If -1, then the number of jobs is set to the
        number of cores.

    Attributes
    ----------
    decision_scores_ : numpy array of shape (n_samples,)
        The outlier scores of the training data.
        The higher, the more abnormal. Outliers tend to have higher
        scores. This value is available once the detector is
        fitted.
    threshold_ : float
        The threshold is based on ``contamination``. It is the
        ``n_samples * contamination`` most abnormal samples in
        ``decision_scores_``. The threshold is calculated for generating
        binary outlier labels.
    labels_ : int, either 0 or 1
        The binary labels of the training data. 0 stands for inliers
        and 1 for outliers/anomalies. It is generated by applying
        ``threshold_`` on ``decision_scores_``.
    """

    def __init__(self, contamination=0.1, n_jobs=1):
        super(ECOD, self).__init__(contamination=contamination)
        self.n_jobs = n_jobs

    def fit(self, X, y=None):
        """Fit detector. y is ignored in unsupervised methods.
        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.
        y : Ignored
            Not used, present for API consistency by convention.
        Returns
        -------
        self : object
            Fitted estimator.
        """
        X = check_array(X)
        self._set_n_classes(y)
        self.decision_scores_ = self.decision_function(X)
        self.X_train = X
        self._process_decision_scores()
        return self

    def decision_function(self, X):
        """Predict raw anomaly score of X using the fitted detector.
         For consistency, outliers are assigned with larger anomaly scores.
        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The training input samples. Sparse matrices are accepted only
            if they are supported by the base estimator.
        Returns
        -------
        anomaly_scores : numpy array of shape (n_samples,)
            The anomaly score of the input samples.
        """
        # use multi-thread execution
        if self.n_jobs != 1:
            return self._decision_function_parallel(X)
        if hasattr(self, 'X_train'):
            original_size = X.shape[0]
            X = np.concatenate((self.X_train, X), axis=0)
        self.U_l = -1 * np.log(column_ecdf(X))
        self.U_r = -1 * np.log(column_ecdf(-X))

        skewness = np.sign(skew(X, axis=0))
        self.U_skew = self.U_l * -1 * np.sign(
            skewness - 1) + self.U_r * np.sign(skewness + 1)

        self.O = np.maximum(self.U_l, self.U_r)
        self.O = np.maximum(self.U_skew, self.O)

        if hasattr(self, 'X_train'):
            decision_scores_ = self.O.sum(axis=1)[-original_size:]
        else:
            decision_scores_ = self.O.sum(axis=1)
        return decision_scores_.ravel()

    def _decision_function_parallel(self, X):
        """Predict raw anomaly score of X using the fitted detector.
         For consistency, outliers are assigned with larger anomaly scores.
        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The training input samples. Sparse matrices are accepted only
            if they are supported by the base estimator.
        Returns
        -------
        anomaly_scores : numpy array of shape (n_samples,)
            The anomaly score of the input samples.
        """
        if hasattr(self, 'X_train'):
            original_size = X.shape[0]
            X = np.concatenate((self.X_train, X), axis=0)

        n_samples, n_features = X.shape[0], X.shape[1]

        if n_features < 2:
            raise ValueError(
                'n_jobs should not be used on one dimensional dataset')

        if n_features <= self.n_jobs:
            self.n_jobs = n_features
            warnings.warn("n_features <= n_jobs; setting them equal instead.")

        n_jobs, n_dims_list, starts = _partition_estimators(n_features,
                                                            self.n_jobs)

        all_results = Parallel(n_jobs=n_jobs, max_nbytes=None,
                               verbose=True)(
            delayed(_parallel_ecdf)(
                n_dims_list[i],
                X[:, starts[i]:starts[i + 1]],
            )
            for i in range(n_jobs))

        # recover the results
        self.U_l = np.zeros([n_samples, n_features])
        self.U_r = np.zeros([n_samples, n_features])

        for i in range(n_jobs):
            self.U_l[:, starts[i]:starts[i + 1]] = all_results[i][0]
            self.U_r[:, starts[i]:starts[i + 1]] = all_results[i][1]

        self.U_l = -1 * np.log(self.U_l)
        self.U_r = -1 * np.log(self.U_r)

        skewness = np.sign(skew(X, axis=0))
        self.U_skew = self.U_l * -1 * np.sign(
            skewness - 1) + self.U_r * np.sign(skewness + 1)

        self.O = np.maximum(self.U_l, self.U_r)
        self.O = np.maximum(self.U_skew, self.O)

        if hasattr(self, 'X_train'):
            decision_scores_ = self.O.sum(axis=1)[-original_size:]
        else:
            decision_scores_ = self.O.sum(axis=1)
        return decision_scores_.ravel()

    def explain_outlier(self, ind, columns=None, cutoffs=None,
                        feature_names=None, file_name=None,
                        file_type=None):  # pragma: no cover
        """Plot dimensional outlier graph for a given data point within
        the dataset.

        Parameters
        ----------
        ind : int
            The index of the data point one wishes to obtain
            a dimensional outlier graph for.

        columns : list
            Specify a list of features/dimensions for plotting. If not
            specified, use all features.

        cutoffs : list of floats in (0., 1), optional (default=[0.95, 0.99])
            The significance cutoff bands of the dimensional outlier graph.

        feature_names : list of strings
            The display names of all columns of the dataset,
            to show on the x-axis of the plot.

        file_name : string
            The name to save the figure

        file_type : string
            The file type to save the figure

        Returns
        -------
        Plot : matplotlib plot
            The dimensional outlier graph for data point with index ind.
        """
        if columns is None:
            columns = list(range(self.O.shape[1]))
            column_range = range(1, self.O.shape[1] + 1)
        else:
            column_range = range(1, len(columns) + 1)

        cutoffs = [1 - self.contamination,
                   0.99] if cutoffs is None else cutoffs

        # plot outlier scores
        plt.scatter(column_range, self.O[ind, columns], marker='^', c='black',
                    label='Outlier Score')

        for i in cutoffs:
            plt.plot(column_range,
                     np.quantile(self.O[:, columns], q=i, axis=0),
                     '--',
                     label='{percentile} Cutoff Band'.format(percentile=i))
        plt.xlim([1, max(column_range)])
        plt.ylim([0, int(self.O[:, columns].max().max()) + 1])
        plt.ylabel('Dimensional Outlier Score')
        plt.xlabel('Dimension')

        ticks = list(column_range)
        if feature_names is not None:
            assert len(feature_names) == len(ticks), \
                "Length of feature_names does not match dataset dimensions."
            plt.xticks(ticks, labels=feature_names)
        else:
            plt.xticks(ticks)

        plt.yticks(range(0, int(self.O[:, columns].max().max()) + 1))
        plt.xlim(0.95, ticks[-1] + 0.05)
        label = 'Outlier' if self.labels_[ind] == 1 else 'Inlier'
        plt.title(
            'Outlier score breakdown for sample #{index} ({label})'.format(
                index=ind + 1, label=label))
        plt.legend()
        plt.tight_layout()

        # save the file if specified
        if file_name is not None:
            if file_type is not None:
                plt.savefig(file_name + '.' + file_type, dpi=300)
            # if not specified, save as png
            else:
                plt.savefig(file_name + '.' + 'png', dpi=300)
        plt.show()

        # todo: consider returning results
        # return self.O[ind, columns], self.O[:, columns].quantile(q=cutoffs[0], axis=0), self.O[:, columns].quantile(q=cutoffs[1], axis=0)
